Abstract
The aim of the chapter was to model the impact of selected determinants (trade openness, human capital, entrepreneurship, and innovation) on regional competitiveness, as well as to propose future activities and measures required to be implemented to improve the competitive performance of the regions. The research was conducted on the sample of 18 regions in six European countries: Serbia, Croatia, Slovenia, Northern Macedonia, Montenegro, and Romania. The database was prepared, and the statistical processing was performed in SPSS. In this data analysis, the following methods were used: comparative analysis, correlation, and regression analysis. The results of the research showed that the impact of the determinants—import dependence, the number of pupils enrolled in secondary education, gross domestic expenditure on research and development, and the number of companies per 10,000 inhabitants on the competitiveness of the region—are (statistically) significant.
TopIntroduction
What are the definitions of competitiveness, in the literature?. According to the definition of World Economic Forum, competitiveness is a set of institutions, policies and factors that determine the level of productivity of a country (Schwab & Porter, 2018; Krstić et al., 2020b; Krstić et al., 2016b; Radivojević et al., 2019b). At the micro level, competitiveness is defined as the ability of firms to compete, grow, and be profitable (Martin et al., 2006; Powell, 2001), or the ability of a firm to produce and sell products and services at a price lower than the competition (International Institute of Management Development, 2000). Between micro and macro level of competitiveness, regional competitiveness is defined.
One of the most frequently used definitions of regional competitiveness is the European Commission’s definition, according to which, „the competitiveness of a region is its ability to produce goods and services that meet the requirements of the domestic and world market in terms of price, quality, etc., maintaining a high and sustainable level of income, or, more generally, the region’s ability to generate, under external competitive pressures, a relatively high level of income and employment” (European Commission, 1999, p. 75).
What needs to be pointed out is that the concept of regional competitiveness has expanded over time, so that it includes the potential (or the strength) of the region or locality to create a sufficient level of export to achieve a sustainable level of income (and full employment) of the population. On the other hand, regional competitiveness is observed and analyzed as a result of the influence of several factors, and the most important are: (1) the business infrastructure; (2) availabilityand quality of human resources; (3) the production environment, etc. (European Commission, 2004).
Kitson and co-workers suggest that although theorists often use the term “regional competitiveness”, it remains complex and controversial. „We are far from a consensus on what is meant by this term“ (Kitson et al., 2004, p. 992). This is confirmed by numerous definitions of regional competitiveness that can be found in the literature. For example, Huggins believes that regional (or the local) competitiveness refers to conditions that allow companies to compete in selected markets and create value within a particular region (Huggins, 2003). Imre Lengyel and Mikosh Lukovic gave an overview of the competitiveness of Hungarian regions, using indicators, such as: GDP per capita, labor productivity, employment rate, etc. (Lengyel & Lukovic, 2006). Huggins and Davies created the European Competitiveness Index that measures the competitiveness of 27 European countries and 118 regions. In the report, the authors emphasize the role of knowledge, creativity and infrastructure for the analysis of regional competitiveness (Huggins & Davies, 2006). The Polish Regional Competitiveness index was calculated by Bronisz, Heiman and Miszczuk. They ranked 16 NUTS 2 Polish regions based on the weighting system used in the calculation of the final value of the Regional Competitiveness Index (Bronisz et al., 2008).
Key Terms in this Chapter
Regression Analysis: As a term is related to determining the mutual relations between two or more phenomena. For example, we may be interested in the relationship between the time spent preparing for the exam and the grade obtained on the exam, employees' salaries and their education, interest rates and money supply. In order to determine whether and to what extent these phenomena are dependent, we make regressions model. Regression analysis has a wide application in predicting and forecasting phenomena in various fields, such as economics, medicine, psychology, history.
Education: Is the process of transferring the knowledge and culture of humanity from generation to generation. The basic goal of education is to create a healthy society that excels high level of information. Education is responsible for changes in society and must be obey change more than any other segment of society.
Innovation: Is a change, a novelty or a process of making changes. In the humanities, the term refers to the process of modernization and positive change in services or their results. Innovation is the application of a new and improved idea, procedure, good, service, process that brings new benefits or quality in application. Innovations in a broader sense bring improvements in the field of product construction (technological innovations), process innovations, organization of work or business, marketing, service innovations, etc.
Human Capital: Refers to the abilities, skills, and knowledge available to the total population, which are accumulated through formal education, experience in the labor market, additional education and the like.
Statistical Significance: Implies the decision whether the observed relationship between two or more variables was created by the action of a case or was created by the action of some experimental factor. In the social sciences, it is common to use a significance level of 0.05. This practically means that there is a 5% probability that the observed relationship between the variables is due to the action of the case. If 5% is considered to be a large value, a significance level of 0.01 (1%) can be selected. The level of significance is denoted by a, the Greek letter alpha.